Shadow Removal for Greyscale Video Sequences

2012 ◽  
Vol 17 (4) ◽  
pp. 117-129
Author(s):  
Tomasz Kryjak
Keyword(s):  
Video Surveillance ◽  
Shadow Detection ◽  
Video Sequences ◽  
Automatic Method ◽  
Shadow Removal ◽  
Rectangular Grid ◽  
Current Frame ◽  
Elimination Algorithm ◽  
Object Edge ◽  
Binarization Threshold

Abstract The paper presents a shadow detection and elimination algorithm designed for greyscale video sequences. The paper proposes: an automatic method for determining the binarization threshold on the basis of the object edge analysis, division of areas identified as potential shadow using a rectangular grid, analyzing the similarities between the current frame and the background model performed in areas and analyzing the potential shadows areas position relative to the position of areas identified as a true object. The algorithm was designed to eliminate shadows casted by people in video surveillance sequences. The obtained results show the usefulness of the proposed solution.

Anomaly Detection in Video Surveillance via Gaussian Process

2015 ◽  
Vol 29 (06) ◽  
pp. 1555011 ◽  
Author(s):  
Nannan Li ◽  
Xinyu Wu ◽  
Huiwen Guo ◽  
Dan Xu ◽  
Yongsheng Ou ◽  
...  
Keyword(s):  
Anomaly Detection ◽  
Regression Model ◽  
Gaussian Process ◽  
Video Surveillance ◽  
Gaussian Process Regression ◽  
Bayesian Regression ◽  
Current Frame ◽  
Motion Patterns ◽  
Online Clustering ◽  
Efficient Calculation

In this paper, we propose a new approach for anomaly detection in video surveillance. This approach is based on a nonparametric Bayesian regression model built upon Gaussian process priors. It establishes a set of basic vectors describing motion patterns from low-level features via online clustering, and then constructs a Gaussian process regression model to approximate the distribution of motion patterns in kernel space. We analyze different anomaly measure criterions derived from Gaussian process regression model and compare their performances. To reduce false detections caused by crowd occlusion, we utilize supplement information from previous frames to assist in anomaly detection for current frame. In addition, we address the problem of hyperparameter tuning and discuss the method of efficient calculation to reduce computation overhead. The approach is verified on published anomaly detection datasets and compared with other existing methods. The experiment results demonstrate that it can detect various anomalies efficiently and accurately.

scholarly journals Implementation of Robust Tracking Algorithm on Nano-Computer

2018 ◽  
Vol 11 (2) ◽  
pp. 446
Author(s):  
Khaled Hammemi ◽  
Mohamed Atri
Keyword(s):  
Video Surveillance ◽  
Visual Servoing ◽  
Active Vision ◽  
Field Of View ◽  
Video Sequences ◽  
Partial Occlusion ◽  
Robust Tracking ◽  
Arm Processor ◽  
Probabilistic Process ◽  
Require Data

<p>In this work, we developed the NSSD-DT method, which allows us to track a target in a robust way. This method effectively overcomes the problems of geometrical deformation of the target, partial occlusion and allows recovery after the target leaves the field of view. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require data-based beforehand. Experimental results on several difficult video sequences have proven performance benefits. The algorithm is implemented on a BCS 2835 system based on a quad core ARM processor, it is also compared to the software solution. NSSD-DT can be used in several applications such as video surveillance, active vision or industrial visual servoing.</p>

scholarly journals Traffic extreme situations detection in video sequences based on integral optical flow

2019 ◽  
Vol 43 (4) ◽  
pp. 647-652 ◽  
Author(s):  
H. Chen ◽  
S. Ye ◽  
A. Nedzvedz ◽  
O. Nedzvedz ◽  
H. Lv ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Surveillance ◽  
Real World ◽  
Road Traffic ◽  
Video Sequences ◽  
Surveillance Systems ◽  
Directional Movement ◽  
Background Motion ◽  
Random Background ◽  
Integral Optical Flow

Road traffic analysis is an important task in many applications and it can be used in video surveillance systems to prevent many undesirable events. In this paper, we propose a new method based on integral optical flow to analyze cars movement in video and detect flow extreme situations in real-world videos. Firstly, integral optical flow is calculated for video sequences based on optical flow, thus random background motion is eliminated; secondly, pixel-level motion maps which describe cars movement from different perspectives are created based on integral optical flow; thirdly, region-level indicators are defined and calculated; finally, threshold segmentation is used to identify different cars movements. We also define and calculate several parameters of moving car flow including direction, speed, density, and intensity without detecting and counting cars. Experimental results show that our method can identify cars directional movement, cars divergence and cars accumulation effectively.

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